Identifying High Influential Users in Social Media by Analyzing Users’ Behaviors

Author:

Zhao Zhongying12,Zhou Hui1,Zhang Bijun1,Ji Fujiao1,Li Chao1

Affiliation:

1. College of Computer Science and Engineering, Shandong Province Key laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao, China

2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference29 articles.

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2. ĺęva Tardos, Maximizing the spread of influence through a social network;Kempe;ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2015

3. Wang Y. , Cong G. , Song G. and Xie K. , Community-based greedy algorithm for mining top-k influential nodes in mobile social networks, in: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, pp. 1039–1048.

4. Your friends have more friends than you do: Identifying influential mobile users through random-walk sampling;Han;IEEE/ACM Transactions on Networking,2014

5. Mikolov T. , Chen K. , Corrado G. , Dean J. , Efficient estimation of word representations in vector space, CoRR abs/1301.3781.

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